Rocket Close transforms mortgage document processing with Amazon Bedrock and Amazon Textract

2026-04-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

Rocket Close, a Detroit-based title and appraisal management company, partnered with the AWS Generative AI Innovation Center (GenAIIC) to automate its mortgage document processing, transforming a manual workflow that took 10 hours per package into an efficient solution. Processing approximately 2,000 abstract package files daily, each averaging 75 pages, the company faced significant volume overload and scalability limits. The developed solution, utilizing Amazon Textract for OCR and Amazon Bedrock for foundation models, achieved a 90% overall accuracy in document segmentation, classification, and field extraction. This automation reduced processing time by 15 times, from 30 minutes to under 2 minutes per package, and is designed to scale to over 500,000 documents annually, positioning Rocket Close for sustainable growth and faster customer service.

Key takeaway

For AI Engineers and MLOps Engineers tasked with automating high-volume, document-intensive workflows, consider a two-stage pipeline leveraging AWS services like Amazon Textract and Amazon Bedrock. Focus on advanced prompt engineering and deep domain knowledge integration to achieve high accuracy and significant processing speed improvements, ensuring your solution scales effectively for enterprise needs.

Key insights

Generative AI, combined with OCR, can automate complex document processing with high accuracy and speed.

Principles

• Prompt engineering is critical for LLM accuracy.
• Domain knowledge enhances extraction performance.
• Two-stage pipeline optimizes OCR and LLM tasks.

Method

A two-stage pipeline uses Amazon Textract for OCR and markdown conversion, then Amazon Bedrock FMs with advanced prompt engineering and domain knowledge for document classification, segmentation, and data extraction into JSON.

In practice

• Use Amazon Textract for diverse document OCR.
• Integrate domain-specific glossaries and data dictionaries.
• Develop tailored evaluation metrics for field types.

Topics

• Mortgage Document Processing
• Amazon Textract
• Amazon Bedrock
• Generative AI
• Prompt Engineering

Best for: AI Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.